Speech signal acquisition methods based on compressive sensing

2015 ◽  
pp. 125-130
Keyword(s):  
Compressive Sensing ◽  
Speech Signal ◽  
Signal Acquisition

scholarly journals Compressive Sensing in Signal Processing: Algorithms and Transform Domain Formulations

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Irena Orović ◽  
Vladan Papić ◽  
Cornel Ioana ◽  
Xiumei Li ◽  
Srdjan Stanković
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Compressive Sensing ◽  
Signal Reconstruction ◽  
Signal Acquisition ◽  
Transform Domain ◽  
Hermite Transform ◽  
Time Frequency ◽  
Reconstruction Methods ◽  
Fourier Transform Domain

Compressive sensing has emerged as an area that opens new perspectives in signal acquisition and processing. It appears as an alternative to the traditional sampling theory, endeavoring to reduce the required number of samples for successful signal reconstruction. In practice, compressive sensing aims to provide saving in sensing resources, transmission, and storage capacities and to facilitate signal processing in the circumstances when certain data are unavailable. To that end, compressive sensing relies on the mathematical algorithms solving the problem of data reconstruction from a greatly reduced number of measurements by exploring the properties of sparsity and incoherence. Therefore, this concept includes the optimization procedures aiming to provide the sparsest solution in a suitable representation domain. This work, therefore, offers a survey of the compressive sensing idea and prerequisites, together with the commonly used reconstruction methods. Moreover, the compressive sensing problem formulation is considered in signal processing applications assuming some of the commonly used transformation domains, namely, the Fourier transform domain, the polynomial Fourier transform domain, Hermite transform domain, and combined time-frequency domain.

Multidimensional feature diversity based speech signal acquisition

2020 ◽  
Vol 23 (3) ◽  
pp. 527-535
Author(s):  
Ashok Kumar Konduru ◽  
J. L. Mazher Iqbal
Keyword(s):  
Speech Signal ◽  
Signal Acquisition

scholarly journals Compressive sensing: A methodological approach to an efficient signal processing

DYNA ◽  
2015 ◽  
Vol 82 (192) ◽  
pp. 203-210 ◽  
Author(s):  
Evelio Astaiza Hoyos ◽  
Pablo Emilio Jojoa Gómez ◽  
Héctor Fabio Bermúdez Orozco
Keyword(s):  
Signal Processing ◽  
Dimensionality Reduction ◽  
Compressive Sensing ◽  
Methodological Approach ◽  
Signal Acquisition ◽  
Methodological Issues ◽  
New Paradigm ◽  
Signal Acquisition And Processing

Compressive Sensing (CS) is a new paradigm for signal acquisition and processing, which integrates sampling, compression, dimensionality reduction and optimization, which has caught the attention of a many researchers; SC allows the reconstruction of dispersed signals in a given domain from a set of measurements could be described as incomplete, due to that the rate at which the signal is sampled is much smaller than Nyquist's rate. This article presents an approach to address methodological issues in the field of processing signals from the perspective of SC.

scholarly journals The Pros and Cons of Compressive Sensing for Wideband Signal Acquisition: Noise Folding versus Dynamic Range

2012 ◽  
Vol 60 (9) ◽  
pp. 4628-4642 ◽  
Author(s):  
Mark A. Davenport ◽  
Jason N. Laska ◽  
John R. Treichler ◽  
Richard G. Baraniuk
Keyword(s):  
Compressive Sensing ◽  
Dynamic Range ◽  
Signal Acquisition ◽  
Wideband Signal ◽  
Pros And Cons ◽  
Noise Folding

scholarly journals Sparse GLONASS Signal Acquisition Based on Compressive Sensing and Multiple Measurement Vectors

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Guodong He ◽  
Maozhong Song ◽  
Shanshan Zhang ◽  
Peng Song ◽  
Xinwen Shu
Keyword(s):  
Compressive Sensing ◽  
Satellite System ◽  
New Method ◽  
Signal Acquisition ◽  
Multiple Measurement ◽  
Multiple Measurement Vectors ◽  
Navigation Data ◽  
Long Time ◽  
Global Navigation Satellite ◽  
Sparse Features

A sparse global navigation satellite system (GLONASS) signal acquisition method based on compressive sensing and multiple measurement vectors is proposed. The nonsparse GLONASS signal can be represented sparsely on our proposed dictionary which is designed based on the signal feature. Then, the GLONASS signal is sensed by a normalized orthogonal random matrix and acquired by the improved multiple measurement vectors acquisition algorithm. There are 10 cycles of pseudorandom codes in a navigation message, and these 10 pseudorandom codes have the same row sparse structure. So, the acquisition probability can be raised by row sparse features theoretically. A large number of simulated GLONASS signal experiments show that the acquisition probability increases with the increase in the measurement vector column dimension. Finally, the practical availability of the new method is verified by acquisition experiments with the real record GLONASS signal. The new method can reduce the storage space and energy loss of data transmission. We hope that the new method can be applied to field receivers that need to record and transmit navigation data for a long time.

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